Fred Hutch Oriented DaSL Training

Updated: October 16, 2023

Edit this Page via GitHub       Comment by Filing an Issue      Have Questions? Ask them here.

The Fred Hutch Data Science Lab’s wider training efforts can be found on our training page. However, in addition to data science training courses we create that are focused on a wider audience, we have also created a number of Fred Hutch oriented training materials.

Course Name Description Link(s)
Cluster 101 Intro to using the Fred Hutch HPC cluster for new or experienced users. Provides a certification option. With Certification or Without Certification
Developing WDL Workflows A hands-on guide to developing WDL workflows Course link
Code Review Leading a lab with novice or experienced code writers and users? Either way, our Code Review guidance materials include helpful suggestions for various types of lab members, expertise and group dynamics. Course Link
NIH Data Sharing We have created and are actively developing a guide that walks you through the process of complying with the new 2023 NIH Data Sharing Policy. Guide Link

Cluster 101

We collaborated with SciComp and created a course called Cluster 101, to introduce new and experienced high performance computing users to the Fred Hutch cluster. This course can be taken anytime, anywhere, for free (don’t worry just set the LeanPub cost to zero) and it’ll help you check to make sure your account is set up to use the cluster and either help you check to make sure you understand the basics of how to use a cluster, or help you get up to speed. It can even give you a certification for reporting between staff and lab leaders if need be.

  • If you need/want to get a certification, please take the course through Leanpub at this link.

  • If you do not need the certification or want to bookmark the course for future reference, you can find the material at this link.

If you take this course and want to give us feedback or would like to learn more about it, you can share your thoughts in Slack in the #ask-dasl channel) or you can file an issue on the course’s GitHub repository.

Developing WDL (Workflow Description Language) Workflows

The Developing WDL Workflows Guide, serves as a comprehensive quick start manual for crafting workflows using the Workflow Description Language (WDL). This guide is intended for audiences familiar with building bioinformatics workflows and looking to quickly learn the essentials of writing their first WDL workflow. This guide will take you through the creation of a WDL workflow to call somatic mutations from cancer cell-line data and in the process highlight essential elements of WDL workflow design along the way. This guide not only showcases effective strategies for structuring and parallelizing workflows to enhance readability and reproducibility but also explores various methods for optimizing workflow performance. Additionally, it provides insights into managing different computational backends to run WDL workflows.

If you use this guide and want to give us feedback or would like to learn more about it, you can share your thoughts in Slack in the #ask-dasl channel) or you can file an issue on the guide’s GitHub repository.

Code Review

Leading a lab with novice or experienced code writers and users? Either way, see our Code Review materials that include helpful suggestions for various types of lab members, expertise and group dynamics.

If you take this course and want to give us feedback or would like to learn more about it, you can share your thoughts in Slack in the #ask-dasl channel) or you can file an issue on the course’s GitHub repository.

NIH Data Sharing

We have created and are actively developing a guide you can find here that walks you through the process of complying with the new 2023 NIH Data Sharing Policy.

If you take this course and want to give us feedback or would like to learn more about it, you can share your thoughts in Slack in the #ask-dasl channel) or you can file an issue on the course’s GitHub repository.

Updated: October 16, 2023

Edit this Page via GitHub       Comment by Filing an Issue      Have Questions? Ask them here.